Systems and methods for managing smart alarms

ABSTRACT

A method for managing smart alarms in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device of a monitoring and control system to identify at least one of power events in the electrical system, or alarms triggered in response to any identified power events. Information related to the identified power events and the identified alarms may be aggregated, and relevant event and/or alarm management groups and/or relevant event and/or alarm periods may be identified from the aggregated information. One or more actions may be triggered, avoided or avoid triggering in response to the identified event management groups and/or the identified event and/or alarm periods. Systems for managing smart alarms are also provided herein.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional ApplicationSer. No. 62/652,844 which was filed on Apr. 4, 2018, U.S. ProvisionalApplication Ser. No. 62/785,291 which was filed on Dec. 27, 2018 andU.S. Provisional Application Ser. No. 62/788,532 which was filed on Jan.4, 2019, all of the applications of which are incorporated by referenceherein in their entirety.

FIELD

This disclosure relates generally to smart alarms, and moreparticularly, to systems and methods related to managing smart alarms inan electrical or power system.

BACKGROUND

The changing world of energy is making it increasingly challenging tooptimize power reliability, energy costs, and operational efficiencysuch as in critical power environments (e.g., hospitals, data centers,airports, and manufacturing facilities). Utility power grids arebecoming more dynamic and facility power distribution systems arebecoming more complex and sensitive to power quality issues due toincreasing electronic control devices, threatening network stability.Competitive pressures and environmental regulations are pushingexpectations for energy efficiency and business sustainability higherthan ever. Addressing these challenges requires new digital toolsdesigned specifically to enable faster response to opportunities andrisks related to electrical/power system reliability and operationalstability.

Power quality disturbances are a primary cause of unexpected businessdowntime and equipment malfunction/damage/failure. According to someestimates, power quality disturbances are responsible for 30-40% ofbusiness downtime, and power quality problems cost companies roughly 4%of their annual revenue. Examples of the detrimental effects toequipment that may be attributed to power quality disturbances includeoverheating of equipment components (e.g., motors, capacitors, cables,transformers, etc.), accelerated wear and tear, premature aging ofequipment components, malfunctions and mis-operations, and erroneouscircuit breaker or relays operations.

The economic impact produced by power quality disturbances may includeincreased energy bills, additional financial penalties (e.g., penaltiesas a result of power disruption), and potentially detrimental impacts onthe environment (e.g., increased carbon footprint). Power qualitydisturbances may also adversely result in increased charges related todemand, increases in electrical/power system losses, and increases involtage drops. Three examples of areas that are influenced by powerquality disturbances include: uptime, asset condition, and energyefficiency. For example, system uptime may be affected by electricalinstallations intentionally or inadvertently being removed from servicedue to voltage sags, interruptions, and/or undervoltage/overvoltageconditions. Moreover, nuisance trips of circuits caused by harmonics,voltage swells, or transients may also lead to reduced uptime. Assets(e.g., cables, transformers, capacitor banks, etc.) may be detrimentallyaffected by power quality disturbances or conditions. For instance,overheating of equipment, an unplanned change in design characteristics,and/or a decreased service life are just a few impacts caused by powerquality anomalies. Finally, the efficient use of energy is also affectedby power quality disturbances.

According to a specific example, capacitor banks may be affected bypower quality disturbances (e.g., harmonics) which are characterized asa steady-state distortion of the voltage and/or current signals.Non-linear power loads from electric arc furnaces (EAFs), electricrailways, thyristor-based voltage and frequency modifying devices havebecome primary harmonic sources in a power grid. These systems injectlarge amounts of harmonic currents into the power system, leading todistortion of the fundamental current signal in the power grid.Harmonics may adversely impact the normal operations of capacitor banksin numerous ways (e.g., increasing power losses, producing harmonicresonance, increasing harmonic currents, and reducing the service lifeof the capacitor bank through additional heating).

Proper collection and interpretation of events, alarms, power qualitydata and other associated data about the system (e.g., contextual datafrom manufacturing process SCADA, building management system (BMS) andthe user interactions, user defined priority levels, etc.) may allow forboth businesses and energy providers to discriminate useful informationfrom collected events, improve electrical/power system operations,recovery time and efficiency, and limit detrimental effects from powerquality disturbances and other undesirable conditions on theelectrical/power system or facilities. Energy procurement managers mayuse power quality data to identify and avoid penalties or torevise/update energy contracts. Likewise, maintenance engineers may usepower quality data to properly diagnose equipment issues and improveroot cause analysis and reduce equipment downtime.

SUMMARY

Described herein are systems and methods related to managing smartalarms in an electrical or power system. In one aspect of thisdisclosure, a method for managing smart alarms in an electrical systemincludes processing electrical measurement data from or derived fromenergy-related signals captured or derived by at least one intelligentelectronic device (IED) of a monitoring and control system (MCS) toidentify power events in the electrical system, and to identify alarmstriggered in response to the identified power events. Informationrelated to the identified power events and the identified alarms may beaggregated, and relevant event management groups and/or relevant eventand/or alarm periods may be identified (e.g., automatically or manually)from the aggregated information. In some embodiments, the eventmanagement groups include groups and/or sequences of the identifiedpower events, and/or groups and/or sequences of alarm events triggeredin response to the identified alarms. One or more actions may betriggered, avoided or postponed in response to the identified eventmanagement groups and/or event and/or alarm periods in some embodiments.

In some embodiments, the method may be implemented using one or moreIEDs of the at least one IED. Additionally, in some embodiments themethod (or portions thereof) may be implemented remote from the at leastone IED, for example, on a diagnostic computing system and/or on otherportions of the MCS. In some embodiments, the at least one IED may becoupled to measure energy-related signals, receive electricalmeasurement data from or derived from the energy-related signals at aninput, and configured to generate at least one or more outputs. Theoutputs may be used to identify power events, and to identify alarmstriggered in response to the identified power event, in an electricalsystem. Examples of the at least one IED may include a smart utilitymeter, a power quality meter, and/or another metering device (ordevices). The at least one IED may include breakers, relays, powerquality correction devices, uninterruptible power supplies (UPSs),filters, and/or variable speed drives (VSDs), for example. Additionally,the at least one IED may include at least one virtual meter in someembodiments.

In some embodiments, each IED of the at least one IED is installed orlocated at a respective metering point of a plurality of metering points(e.g., physical or virtual metering points) in the electrical system,and the energy-related signals captured or derived by each IED areassociated with the respective metering point. At least one load (e.g.,electrical equipment or devices) may be installed or located at eachmetering point of the plurality of metering points, for example, andeach IED may be configured to monitor the at least one load installed orlocated at the respective metering point at which the IED is installedor located. In the illustrated example, the energy-related signalscaptured or derived by the IED may be associated with the at least oneload.

Examples of energy-related signals captured or derived by the at leastone IED may include at least one of: voltage, current, energy, activepower, apparent power, reactive power, harmonic voltages, harmoniccurrents, total voltage harmonic distortion, total current harmonicdistortion, harmonic power, individual phase currents, three-phasecurrents, phase voltages, line voltages and power factor.

In some embodiments, identifying power events from electricalmeasurement data from or derived from the energy-related signalsincludes identifying power quality event types of the of the powerevents. The power quality event types may include, for example, at leastone of: a voltage sag, a voltage swell, a voltage or current transient,a temporary interruption, and voltage or current harmonic distortion. Itis understood there are types of power quality events and there arecertain characteristics of these types of power quality events.According to IEEE Standard 1159-2009, for example, a voltage sag is adecrease to between 0.1 and 0.9 per unit (pu) in rms voltage or currentat the power frequency for durations of 0.5 cycle to 1 min. Typicalvalues are 0.1 to 0.9 pu. Additionally, according to IEEE Standard1159-2009, a voltage swell is an increase in rms voltage or current atthe power frequency for durations from 0.5 cycles to 1 min. It isunderstood that IEEE Standard 1159-2009 is one standards body's (IEEE inthis case) way of defining/characterizing power quality events. It isunderstood there are other standards that define power qualitycategories/events as well, such as the International ElectrotechnicalCommission (IEC), American National Standards Institute (ANSI), etc.,which may have different descriptions or power quality event types,characteristics, and terminology. In some embodiments, power qualityevents may be customized power quality events (e.g., defined by a user).

The above method, and the other methods (and systems) described below,may include one or more of the following features either individually orin combination with other features in some embodiments. In someembodiments, at least one of the alarms is triggered in response to theelectrical measurement data being above one or more upper alarmthresholds or below one or more lower alarm thresholds. An anomalousvoltage condition, for example, which is one example type of powerevent, corresponds to a measured IED voltage being above one or moreupper alarm thresholds or below one or more lower alarm thresholds. Insome embodiments, at least one of the alarms is additionally oralternatively triggered in response to multiple power events. Forexample, an alarm may be triggered in response to a sag and aninterruption (or other group of power events) that occur over aparticular time period.

In some embodiments, the information related to the identified powerevents and the identified alarms is aggregated based on at least one of:locations of the identified power events in the electrical system, timeperiod(s) or interval(s), criticality of the identified alarms to aparticular process or application, and device type(s) of the at leastone IED. Additional aspects of aggregation of power events and alarmsare described, for example, in co-pending application numberPCT/US19/25754, entitled “Systems and Methods for Intelligent AlarmGrouping”, which application is assigned to the same assignee as thepresent disclosure, and is incorporated by reference herein in itsentirety.

In some embodiments, discriminant characteristics may be identified inthe aggregated information. Identifying the discriminant characteristicsmay include, for example, identifying breakpoints associated with theevent and/or alarm periods, modeling each of the event and/or alarmperiods, classifying each of the modeled event and/or alarm periods, andidentifying discriminant characteristics in each of the modeled eventand/or alarm periods. In some embodiments, the event and/or alarmperiods may be identified based on detected changes in relevant datafrom the aggregated information. The breakpoints associated with theevent and/or alarm periods may correspond to significant change pointsin the aggregated information separating one event and/or alarm periodfrom a next event and/or alarm period of the event and/or alarm periods,for example.

In some embodiments, modeling each of the event and/or alarm periods,includes determining a best possible model for each of the event and/oralarm periods, and modeling each of the event and/or alarm periods basedon the determined best possible model. The best possible model may bedetermined, for example, by comparing each event and/or alarm period ofthe event and/or alarm periods with a previous event and/or alarm periodof the event and/or alarm periods. As one example, an impact of eachevent and/or alarm period on the electrical system may be compared withthe impact of a previous event and/or alarm period on the electricalsystem to determine the best possible model. For example, a currentday/real-time number of alarms/events may be determined to have manymore alarms/events than any of the previous days over the past fiveyears (or another period of time). Additionally, a current day may bedetermined to have a sequence of events (SoE) which is ten (or anothermultiple) times larger than a previous SoE group. Both could generatethe action of triggering a diagnostic report, for example, showing thediscriminant differences to help identify and focus on what is goingwrong, or at a minimum where something is going wrong, or when thealarms/events started.

In some embodiments, each of the modeled event and/or alarm periods maybe classified, for example, as stable, rising or dropping based on ananalysis of the modeled event and/or alarm periods. Additionally, oralternatively, each of the event and/or alarm periods may be classifiedthough curve fitting techniques, for example, using one or morestatistical or machine learning algorithms to provide an enriched orfiner model. The statistical or machine learning algorithms may modelslope or slope variations of the event and/or alarm periods, forexample. A simple median model (and many other models and/or modelingtechniques) may be used.

In some embodiments, a relative criticality score of each of theidentified discriminant characteristics may be determined, for example,to a process or an application associated with the electrical system. Insome embodiments, the relative criticality score may be determined for aparticular time period. The particular time period may be associatedwith one or more of the event periods and/or alarm periods, for example.In some embodiments, the relative criticality score is based on animpact of the identified discriminant characteristics to the process orthe application over the particular time period. As one example, theimpact of the identified discriminant characteristics may be related totangible or intangible costs associated with the identified discriminantcharacteristics to the process or the application. In some embodiments,the relative criticality score may be used to prioritize responding tothe identified alarms.

In some embodiments, the relevant alarms or actions may be triggered,for example, by comparing a real time or time aggregated status ofnumber and/or type of events and/or alarms and/or groups and/orsequences and/or periods and/or any aggregated information ordiscriminant characteristic, to a derived threshold or type from themodel of any of the event and/or alarm periods. Additionally, in someembodiments the actionable information and recommendations for thesystem users to reduce the number of events/alarms may be derived fromthe groupings and the discriminant characteristics in a report orthrough an IED or any of the components of the MCS or any other systemconnected to the MCS.

In some embodiments, the identified events and/or the identified alarmsare enriched with the normal behavior profiles derived from waveformcaptures associated with the energy-related signals and then are used ascomparison for the discriminant dimensions identification and groupings,for example, using the waveform captures of normal operations (notevents or alerts triggered), and the profiles derived which create“normal profiles” and store these in the digital repository. In someembodiments, these profiles may be lined to loads switching on/off orpower consumption profiles as well as to other systems' status changesor processes. This then provides context to the current application formore complete or more precise diagnostics, recommendations, actions,especially when impacting other systems. This builds the layer ofinterpretation of the alerts/events/alarms, as it provides additionalcontextual information, thus providing more meaning, or help to identifypossible or probable sources (such as using machine learning, Alalgorithms to find most probable source or combination of sources toexplain a change in status or in a change of value).

In some embodiments, the actions that are triggered or postponed inresponse to the identified event management groups or event and/or alarmperiods include at least one of: shutting down or turning on at leastone component in the electrical system, adjusting one or more parametersassociated with the at least one component, selectively interruptingpower at one or more locations in the electrical system, and generatingan alarm or report. The at least one component may include at least oneload (e.g., equipment or device) in the electrical system, for example.If a normal sequence or group of events is a first number of events(e.g., forty events), for example, and a new sequence or group of eventsis second, much larger number of events (e.g., one thousand events) overa particular period (e.g., two seconds), an action (or actions) may betriggered. The actions that are avoided in response to the identifiedevent management groups or event and/or alarm periods may includelaunching a specific process step which would be normal in the schedule,for example.

In some embodiments, the actions are automatically performed by acontrol device of the MCS. The at least one IED may include the controldevice, or the control device may include the at least one IED, in someembodiments. In other embodiments, the at least one IED may becommunicatively coupled with the control device, for example, inembodiments in which the control device includes, corresponds to, or isincluded in a user device or a diagnostic computing system. It isunderstood that the control device may take other forms as will beunderstood by one of ordinary skill in the art.

In another aspect of this disclosure, an MCS for managing smart alarmsin an electrical system is provided. The MCS includes at least one IEDincluding a processor and memory coupled to the processor. The processorand the memory of the at least one IED are configured to: processelectrical measurement data from or derived from energy-related signalscaptured or derived by the at least one IED to identify power events inthe electrical system, and to identify alarms triggered in response tothe identified power events. Additionally, the processor and the memoryof the at least one IED are configured to: aggregate information relatedto the identified power events and the identified alarms and identifyrelevant event management groups and/or relevant event and/or alarmperiods from the aggregated information. The event management groups mayinclude groups and/or sequences of the identified power events, and/orgroups and/or sequences of alarm events triggered in response to theidentified alarms. The processor and the memory of the at least one IEDare further configured to: trigger, avoid or postpone triggering of oneor more actions in response to the identified event management groupsand/or the identified event and/or alarm periods.

In some embodiments, the at least one IED includes a plurality of IEDsarrange in a hierarchical configuration in the electrical system. Insome embodiments, each IED of the plurality of IEDs is communicativelycoupled to other IEDs of the plurality of IEDs, and each IED isconfigured to share electrical measurement data from or derived fromenergy-related signals derived or captured by the IED with the otherIEDs. The shared electrical measurement data may be processed, forexample, to identify the power events in the electrical system, and toidentify the alarms triggered in response to the identified powerevents.

In some embodiments, the MCS includes at least one user device incommunication with the at least one IED. In some embodiments, the atleast one user device is capable of configuring the at least one IED.

In some embodiments, the processor and the memory of the at least oneIED are further configured to: determine a relative criticality score ofeach of the identified discriminant characteristics to a process or anapplication associated with the electrical system over a particular timeperiod. Additionally, in some embodiments the processor and the memoryof the at least one IED are further configured to: use the determinedrelative criticality score to prioritize responding to the identifiedalarms.

In another aspect of this disclosure, an MCS for managing smart alarmsin an electrical system includes at least one IED configured to captureor derive energy-related signals in the electrical system, and adiagnostic computing system (e.g., a cloud-based diagnostic computingsystem) communicatively coupled to the at least one IED. The diagnosticcomputing system includes a processor and memory coupled to theprocessor. The processor and the memory of the diagnostic computingsystem are configured to: process electrical measurement data from orderived from the energy-related signals captured or derived by the atleast one IED to identify power events in the electrical system, and toidentify alarms triggered in response to the identified power events.Additionally, the processor and the memory of the diagnostic computingsystem are configured to: aggregate information related to theidentified power events and the identified alarms and identify relevantevent management groups and/or relevant event and/or alarm periods fromthe aggregated information. The event management groups may includegroups and/or sequences of the identified power events, and/or groupsand/or sequences of alarm events triggered in response to the identifiedalarms. The processor and the memory of the diagnostic computing systemare further configured to: trigger, avoid or postpone triggering of oneor more actions in response to the identified event management groupsand/or the identified event and/or alarm periods.

In some embodiments, the at least one IED includes a plurality of IEDs,and aggregating information related to the identified power events andthe identified alarms includes aggregating information related to theidentified power events and the identified alarms from the plurality ofIEDs.

In some embodiments, the processor and the memory of the diagnosticcomputing system are further configured to: determine a relativecriticality score of each of the identified discriminant characteristicsto a process or an application associated with the electrical systemover a particular time period. Additionally, in some embodiments theprocessor and the memory of the diagnostic computing system are furtherconfigured to: use the determined relative criticality score toprioritize responding to the identified alarms.

As used herein, an IED (e.g., of the above-discussed MCS) is acomputational electronic device optimized to perform a particularfunction or set of functions. As discussed above, examples of IEDsinclude smart utility meters, power quality meters, and other meteringdevices. IEDs may also be imbedded in variable speed drives (VSDs),uninterruptible power supplies (UPSs), circuit breakers, relays,transformers, or any other electrical apparatus. IEDs may be used toperform monitoring and control functions in a wide variety ofinstallations. The installations may include utility systems, industrialfacilities, warehouses, office buildings or other commercial complexes,campus facilities, computing co-location centers, data centers, powerdistribution networks, and the like. For example, where the IED is anelectrical power monitoring device, it may be coupled to (or beinstalled in) an electrical power distribution system and configured tosense and store data as electrical parameters representing operatingcharacteristics (e.g., voltage, current, waveform distortion, power,etc.) of the power distribution system. These parameters andcharacteristics may be analyzed by a user to evaluate potentialperformance, reliability or power quality-related issues. The IED mayinclude at least a controller (which in certain IEDs can be configuredto run one or more applications simultaneously, serially, or both),firmware, a memory, a communications interface, and connectors thatconnect the IED to external systems, devices, and/or components at anyvoltage level, configuration, and/or type (e.g., AC, DC). At leastcertain aspects of the monitoring and control functionality of an IEDmay be embodied in a computer program that is accessible by the IED.

In some embodiments, the term “IED” as used herein may refer to ahierarchy of IEDs operating in parallel and/or tandem. For example, anIED may correspond to a hierarchy of energy meters, power meters, and/orother types of resource meters. The hierarchy may comprise a tree-basedhierarchy, such a binary tree, a tree having one or more child nodesdescending from each parent node or nodes, or combinations thereof,wherein each node represents a specific IED. In some instances, thehierarchy of IEDs may share data or hardware resources and may executeshared software.

Additional objects and advantages will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the present disclosure. Atleast some of these objects and advantages may be realized and attainedby the elements and combinations particularly pointed out in thedisclosure.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the disclosure, as well as the disclosureitself may be more fully understood from the following detaileddescription of the drawings, in which:

FIG. 1 shows an example network system architecture in accordance withembodiments of the disclosure;

FIG. 1A shows an example intelligent electronic device (IED) that may beused in a network system in accordance with embodiments of thedisclosure;

FIG. 1B shows an example configuration of IEDs in accordance withembodiments of the disclosure;

FIG. 2 shows an example method for managing smart alarms in accordancewith embodiments of the disclosure;

FIG. 3 illustrates an example plot that may be generated in accordancewith embodiments of the disclosure;

FIG. 4 shows an example method for identifying discriminantcharacteristics in accordance with embodiments of the disclosure;

FIG. 5 illustrates an example plot that may be generated in accordancewith embodiments of the disclosure;

FIGS. 6-6C shows another example method for identifying discriminantcharacteristics in accordance with embodiments of the disclosure;

FIGS. 7-7G illustrate various plots that may be generated in accordancewith embodiments of the disclosure;

FIG. 8 shows an example method for identifying and analyzingdiscriminant characteristics in accordance with embodiments of thedisclosure;

FIGS. 9-9A illustrate various plots that may be generated in accordancewith embodiments of the disclosure;

FIG. 10 shows an example method for identifying and analyzing sequencesof events/groups in an electrical/power system; and

FIGS. 11-11B and 12 illustrate various plots that may be generated inaccordance with embodiments of the disclosure.

DETAILED DESCRIPTION

The features and other details of the concepts, systems, and techniquessought to be protected herein will now be more particularly described.It will be understood that any specific embodiments described herein areshown by way of illustration and not as limitations of the disclosureand the concepts described herein. Features of the subject matterdescribed herein can be employed in various embodiments withoutdeparting from the scope of the concepts sought to be protected.

Referring to FIG. 1, there is depicted a schematic view of an exemplarynetwork system architecture configured to perform among other thingsintelligent event and/or alarm analysis and management. The architectureincludes an electrical or power monitoring architecture and controlsystem (MCS) including one or more network nodes 126 and user devices114 and 116 to monitor and control equipment or other devices 102, 104and 106 of the facilities 108, 110 and 112. The network systemarchitecture also includes an electrical or power system including apower generation node(s) 122 to supply power to the facilities 108, 110and 112 across a power distribution network of a utility, e.g., powergrids 118 and 120, and the facilities 108, 110 and 112. The facilities108, 110 and 112 can be an automated industrial facility or includeautomated industrial equipment, or be a commercial building oruniversity campus, as one example amongst many others. The systems anddevices in the network architecture can use a local area network (LAN),wide area network (WAN), or internetwork (including the Internet) tocommunicate over a communication network 124. The communication network124 can be a wired and/or wireless network that uses, for example,physical and/or wireless data links to carry network data among (orbetween) the network nodes.

Each network node 126 can include a computer system, such as anintelligent electronic device (IED), to sense, monitor, capture andanalyze energy-related data on the electrical system. In accordance withthe various embodiments, the IED can capture signal waveformsrepresentative of voltage, current, power or other measurable electricalproperty on the electrical system, create power event profiles, performevent analysis to identify power events and additional informationincluding alarms triggered in response to power events, and performother operations as part of the systems and methods for managing smartalarms described herein. The IED can be a smart device such as a smartpower meter or other power equipment, or be incorporated into orassociated with a power meter or other power equipment on the electricalsystem. The architecture can include a plurality of IEDs arranged atdifferent upstream and downstream positions in a hierarchical level orlayer relationship on the electrical system (e.g., as shown in FIG. 1B,as will be discussed below) to monitor, derive or calculate, analyze andshare energy-related information (e.g., measurement data, derived data,event data and additional information, results of event analysis, eventprofiles, etc.) at any desired position along the electrical system,including positions along the grid, between the utility and a facility,and within the facility. Each of the IEDs may be installed at arespective metering point or location of a plurality of metering pointsor locations in the electrical system (e.g., as shown in FIG. 1B, aswill be discussed below).

In some embodiments, a user may view information about the IEDs (e.g.,IED make, model, type, etc.) and data collected by the IEDs (e.g.,energy usage statistics) using at least one of the user devices 114 and116. Additionally, in some embodiments the user may configure the IEDsusing at least one of the user devices 114 and 116. Each user device 114and 116 can include a computing device, for example, a desktop computer,a laptop computer, a handheld computer, a tablet computer, a smartphone, and/or the like. Additionally, each user device 114 and 116 caninclude or be coupled to one or more input/output devices, for example,to facilitate user interaction with the IEDs (e.g., to view informationabout the IEDs).

In some embodiments, the MCS may also include, or be communicativelycoupled to, a diagnostic computing system 125 via the communicationnetwork 124. In some embodiments, the above-discussed IEDs and userdevices 114 and 116 of the MCS may be directly communicatively coupledto the diagnostic computing system 125. In other embodiments, the IEDsand user devices 114 and 116 may be indirectly communicatively coupledto the diagnostic computing system 125, for example, through anintermediate device, such as a cloud-connected hub or a gateway. Thecloud-connected hub (or the gateway) may, for example, provide the IEDsand the user devices 114 and 116 with access to the diagnostic computingsystem 125.

The diagnostic computing system 125 may be an example of a cloudcomputing system, or cloud-connected computing system. In embodiments,the diagnostic computing system may be a server located within one ormore of the facilities 108, 110 and 112, or may be remotely-locatedcloud-based service. The diagnostic computing system 125 may includecomputing functional components similar to those of the IEDs is someembodiments, but may generally possess greater numbers and/or morepowerful versions of components involved in data processing, such asprocessors, memory, storage, interconnection mechanisms, etc. Thediagnostic computing system 125 can be configured to implement a varietyof analysis techniques to identify patterns in received measurement datafrom the IEDs, as discussed further below. The various analysistechniques discussed herein further involve the execution of one or moresoftware functions, algorithms, instructions, applications, andparameters, which are stored on one or more sources of memorycommunicatively coupled to the diagnostic computing system 125. Incertain embodiments, the terms “function”, “algorithm”, “instruction”,“application”, or “parameter” may also refer to a hierarchy offunctions, algorithms, instructions, applications, or parameters,respectively, operating in parallel and/or tandem. A hierarchy maycomprise a tree-based hierarchy, such a binary tree, a tree having oneor more child nodes descending from each parent node, or combinationsthereof, wherein each node represents a specific function, algorithm,instruction, application, or parameter.

In embodiments, since the diagnostic computing system 125 is connectedto the cloud, it may access additional cloud-connected devices ordatabases (not shown) via the cloud. For example, the diagnosticcomputing system 125 may access historical measurement data previouslyreceived from the at least one IED, historical power event and/or alarmdata, or other data that may be useful in analyzing current measurementdata received from the at least one IED. In embodiments, thecloud-connected devices or databases may correspond to a device ordatabase associated with one or more external data sources.

In embodiments, by leveraging the cloud-connectivity and enhancedcomputing resources of the diagnostic computing system 125 relative tothe IEDs, sophisticated analysis can be performed on data retrieved fromone or more IEDs, as well as on additional sources of data that may bereceived (e.g., from other devices in the electrical system, such ashumidity and temperature sensors), when appropriate. This analysis canbe used to dynamically control one or more parameters, processes,conditions or devices (e.g., 102, 104 and 106) associated with theelectrical system.

In embodiments, the parameters, processes, conditions or equipment aredynamically controlled by one or more control devices of the MCS. Inembodiments, the control devices may correspond to, include, or beincluded one or more of the above-discussed IEDs, diagnostic computingsystem and/or other devices within or external to the electrical system.

It is understood that the network architecture shown in FIG. 1 is butone example network architecture of many potential network architecturesthat may suitable for the systems and methods described herein. Anysuitable network architecture may be used, which allows forcommunication and interaction between components of the MCS (e.g., IEDs,diagnostic computing system, user devices, etc.), equipment or loads,facilities, etc. to perform the operations described herein. Forexample, the systems and methods herein, including but not limited tothe identification of power events, and the identification of alarmstriggered in response to the power events, can be implemented through acloud-based architecture via one or more network nodes.

Referring to FIG. 1A, an example IED 140 that may be suitable for use inthe network system architecture 100 shown in FIG. 1, for example,includes a controller 141, a memory device 142, storage 144, and aninterface 145. The IED 140 also includes an input-output (I/O) port 146,a sensor 147, a communication module 148, and an interconnectionmechanism 143 for communicatively coupling two or more IED components141-148.

The memory device 142 may include volatile memory, such as DRAM or SRAM,for example. The memory device 142 may store programs and data collectedduring operation of the IED 140. For example, in embodiments in whichthe IED 140 is configured to monitor or measure one or more electricalparameters associated with one or more devices or loads in an electricalsystem, the memory device 142 may store the monitored electricalparameters (e.g., from energy-related signals captured or derived by theIED 140).

The storage system 144 may include a computer readable and writeablenonvolatile recording medium, such as a disk or flash memory, in whichsignals are stored that define a program to be executed by thecontroller 141 or information to be processed by the program. Thecontroller 141 may control transfer of data between the storage system144 and the memory device 142 in accordance with known computing anddata transfer mechanisms. In embodiments, the electrical parametersmonitored or measured by the IED 140 may be stored in the storage system144.

The I/O port 146 can be used to couple loads (e.g., 111, shown in FIG.1A) to the IED 140, and the sensor 147 can be used to monitor or measurethe electrical parameters associated with the loads. The I/O port 146can also be used to coupled external devices, such as sensor devices(e.g., temperature and/or motion sensor devices) and/or user inputdevices (e.g., local or remote computing devices) (not shown), to theIED 140. The I/O port 146 may further be coupled to one or more userinput/output mechanisms, such as buttons, displays, acoustic devices,etc., to provide alerts (e.g., to display a visual alert, such as textand/or a steady or flashing light, or to provide an audio alert, such asa beep or prolonged sound) and/or to allow user interaction with the IED140.

The communication module 148 may be configured to couple the IED 140 toone or more external communication networks or devices. These networksmay be private networks within a building in which the IED 140 isinstalled, or public networks, such as the Internet. In embodiments, thecommunication module 148 may also be configured to couple the IED 140 toa cloud-connected hub, or to a cloud-connected central processing unit,associated with a network system architecture including IED 140.

The IED controller 141 may include one or more processors that areconfigured to perform specified function(s) of the IED 140. Theprocessor(s) can be a commercially available processor, such as thewell-known Pentium™, Core™, or Atom™ class processors available from theIntel Corporation. Many other processors are available, includingprogrammable logic controllers. The IED controller 141 can execute anoperating system to define a computing platform on which application(s)associated with the IED 140 can run.

In embodiments, the electrical parameters monitored or measured by theIED 140 may be received at an input of the controller 141 as IED inputdata, and the controller 141 may process the measured electricalparameters to generate IED output data or signals at an output thereof.In embodiments, the IED output data or signals may correspond to anoutput of the IED 140. The IED output data or signals may be provided atI/O port(s) 146, for example. In embodiments, the IED output data orsignals may be received by a diagnostic computing system, for example,for further processing (e.g., to identify power events, as brieflydiscussed above in connection with FIG. 1), and/or by equipment (e.g.,loads) to which the IED is coupled (e.g., for controlling one or moreparameters associated with the equipment, as will be discussed furtherbelow). In one example, the IED 140 may include an interface 145 fordisplaying visualizations indicative of the IED output data or signals.The interface 145 may correspond to a graphical user interface (GUI) inembodiments.

Components of the IED 140 may be coupled together by the interconnectionmechanism 143, which may include one or more busses, wiring, or otherelectrical connection apparatus. The interconnection mechanism 143 mayenable communications (e.g., data, instructions, etc.) to be exchangedbetween system components of the IED 140.

It is understood that IED 140 is but one of many potentialconfigurations of IEDs in accordance with various aspects of thedisclosure. For example, IEDs in accordance with embodiments of thedisclosure may include more (or fewer) components than IED 140.Additionally, in embodiments one or more components of IED 140 may becombined. For example, in embodiments memory 142 and storage 144 may becombined.

Referring to FIG. 1B, an example configuration (e.g., a hierarchicalconfiguration) of IEDs such as IED 140 in an electrical system is shown.As discussed above, an electrical system typically includes one or moremetering points or locations. As also discussed above, one or more IEDsmay be installed or located (temporarily or permanently) at the meteringlocations, for example, to measure, protect and/or control a load orloads in the electrical system.

The illustrated electrical system includes a plurality of meteringlocations (here, M₁, M₂, M₃, etc.). In embodiments in which theelectrical system is a “completely metered” system, for example, atleast one IED is installed at the first metering location M₁, at leastone IED is installed at the second metering location M₂, and so forth.Connection 1 is a physical point in the electrical system where energyflow (as measured at M₁ by the at least one IED installed at M₁)diverges to provide energy to the left electrical system branch(associated with metering locations M₃, M₄, M₇, M₈) and the rightelectrical system branch (associated with metering locations M₂, M₅, M₆,M₉, M₁₀). In accordance with some embodiments of this disclosure, aswill be discussed further below, the IEDs installed at the variousmetering locations (here, M₁, M₂, M₃, etc.) may share electricalmeasurement data from or derived from energy-related signals captured byor derived from the IEDs. The shared electrical measurement data may beused, for example, to identify power events in the electrical system,and to identify alarms triggered in response to the identified powerevents. For example, IEDs installed at metering locations M₇, M₈ mayshare electrical measurement data with an IED installed at meteringlocation M₃ to identify power events at metering location M₃, and toidentify alarms triggered in response to the identified power events atmetering location M₃.

In the illustrated example, the IED installed at metering location M₃ isconsidered to be “upstream” from the IEDs installed at meteringlocations M₇, M₈. Additionally, in the illustrated example, the IEDsinstalled at metering locations M₇, M₈ are considered to be downstreamrelative to the IED installed at metering location M₃. As used herein,the terms “upstream” and “downstream” are used to refer to electricallocations within an electrical system. More particularly, the electricallocations “upstream” and “downstream” are relative to an electricallocation of an IED collecting data and providing this information. Forexample, in an electrical system including a plurality of IEDs, one ormore IEDs may be positioned (or installed) at an electrical locationthat is upstream relative to one or more other IEDs in the electricalsystem, and the one or more IEDs may be positioned (or installed) at anelectrical location that is downstream relative to one or more furtherIEDs in the electrical system. A first IED or load that is positioned onan electrical circuit upstream from a second IED or load may, forexample, be positioned electrically closer to an input or source of theelectrical system (e.g., a utility feed) than the second IED or load.Conversely, a first IED or load that is positioned on an electricalcircuit downstream from a second IED or load may be positionedelectrically closer to an end or terminus of the electrical system thanthe other IED. The above-described first and second IEDs can record anelectrical event's voltage and current phase information (e.g., bysampling the respective signals) and communicatively transmit thisinformation to a diagnostic computing system (e.g., 125, shown in FIG.1). The diagnostic computing system may then analyze the voltage andcurrent phase information (e.g., instantaneous, root-mean-square (rms),waveforms and/or other electrical characteristics) to determine if thesource of the voltage event was electrically upstream or downstream fromwhere the first and/or second IEDs are electrically coupled to theelectrical system (or network), for example, to determine a direction ofa power event (i.e., upstream or downstream).

It is understood that the above-discussed configuration or arrangementof IEDs is but one of many potential configurations of IEDs in anelectrical system.

Referring to FIG. 2 and figures below, several flowcharts (or flowdiagrams) are shown to illustrate various methods of the disclosure.Rectangular elements (typified by element 205 in FIG. 2), as may bereferred to herein as “processing blocks,” may represent computersoftware and/or IED algorithm instructions or groups of instructions.Diamond shaped elements, as may be referred to herein as “decisionblocks,” represent computer software and/or IED algorithm instructions,or groups of instructions, which affect the execution of the computersoftware and/or IED algorithm instructions represented by the processingblocks. The processing blocks and decision blocks can represent stepsperformed by functionally equivalent circuits such as a digital signalprocessor circuit or an application specific integrated circuit (ASIC).

The flowcharts do not depict the syntax of any particular programminglanguage. Rather, the flowcharts illustrate the functional informationone of ordinary skill in the art requires to fabricate circuits or togenerate computer software to perform the processing required of theparticular apparatus. It should be noted that many routine programelements, such as initialization of loops and variables and the use oftemporary variables are not shown. It will be appreciated by those ofordinary skill in the art that unless otherwise indicated herein, theparticular sequence of blocks described is illustrative only and can bevaried. Thus, unless otherwise stated, the blocks described below areunordered; meaning that, when possible, the blocks can be performed inany convenient or desirable order including that sequential blocks canbe performed simultaneously and vice versa. It will also be understoodthat various features from the flowcharts described below may becombined in some embodiments. Thus, unless otherwise stated, featuresfrom one of the flowcharts described below may be combined with featuresof other ones of the flowcharts described below, for example, to capturethe various advantages and aspects of systems and methods associatedwith “smart alarms” sought to be protected by this disclosure.

Referring to FIG. 2, a flowchart illustrates an example method 200 formanaging smart alarms in an electrical/power system. The method 200 maybe implemented, for example, on a processor of at least one IED (e.g.,121, shown in FIG. 1A) in the electrical/power system, a processor of adiagnostic computing system (e.g., 125, shown in FIG. 1) in theelectrical/power system, and/or remote from the at least one IED and thediagnostic computing system, e.g., on a cloud computing device. The atleast one IED, the diagnostic computing system and/or other devices onwhich the method 200 may be implemented may be included, or associatedwith, a monitoring and control system (MCS) in the electrical/powersystem, as discussed above in connection with FIG. 1, for example.

As illustrated in FIG. 2, the method 200 begins at block 205, whereenergy-related signals (or waveforms) are captured or derived by atleast one IED in the electrical/power system. The at least one IED maybe installed or located (temporality or permanently) at respectivemetering location in the electrical/power system, and the energy-relatedsignals may be associated with the respective metering location (or oneor more loads installed at the respective metering location). In someembodiments, the respective metering location may be a respectivemetering location of a plurality of metering locations in theelectrical/power system, for example, in embodiments in which the atleast one IED includes a plurality of IEDs. In these embodiments, theenergy-related signals may be captured or derived by the plurality ofIEDs at each of the respective plurality of metering locations. Forexample, a first one of the IEDs may be installed or located at a firstmetering location in the electrical/power system (e.g., M₁, shown inFIG. 1B), and the energy-related signals captured by the first IED maybe captured at the first metering location, or derived from measurementstaken at the first metering location. Additionally, a second one of theIEDs may be installed or located at a second metering location in theelectrical/power system (e.g., M₂, shown in FIG. 1B), and theenergy-related signals captured by the second IED may be captured at thesecond metering location, or derived from measurements taken at thesecond metering location.

The energy-related signals measured or derived by the at least one IEDmay include, for example, at least one of: voltage, current, energy,active power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages as a fewexamples. It is understood that other types of energy-related signalsmay be captured or derived by the at least one IED.

At block 210, electrical measurement data from or derived from theenergy-related signals captured or derived by the at least one IED atblock 205, is processed (e.g., on the at least one IED, on thediagnostic computing system, and/or remote from the at least one IED andthe diagnostic computing system) to identify power events in theelectrical/power system. The identified power events may be associatedwith the metering location(s) in which the at least one IED isinstalled, a load or loads (e.g., 102, 104, 106, shown in FIG. 1)monitored by the at least one IED, and/or other portions (e.g., remoteportions) or devices of the electrical/power system.

In some embodiments, identifying the power events includes identifyingpower quality event types of the of the power events. The power qualityevent types may include, for example, at least one of: a voltage sag, avoltage swell, a voltage or current transient, a temporary interruption,and voltage or current harmonic distortion as a few examples. It isunderstood that other types of power quality events may be identified.

Identifying the power events may additionally or alternatively includeidentifying a magnitude (or magnitudes) of the power events, a duration(or durations) of the power events, a location (or locations) of thepower events in the electrical/power system, and/or other informationthat may be helpful for identifying alarms (i.e., smart alarms)triggered in response to the identified power events, e.g., at block215, as will be discussed further below. In some embodiments, themagnitude(s), duration(s), location(s) and/or other information may bedetermined based on electrical measurement data from or derived fromenergy-related signals captured or derived by a plurality of IEDs, forexample, in embodiments in which the at least one IED includes aplurality of IEDs located at a respective plurality of metering locationin the electrical/power system. For example, the plurality of IEDs mayshare the energy-related signals captured or derived by the plurality ofIEDs with select ones of the plurality of IEDs (or the diagnosticcomputing system), and the shared energy-related signals may be used todetermine the magnitude(s), duration(s), location(s) and/or otherinformation associated with the identified power event. This may includedetermining differences between the different measured levels ofdisturbances of power quality events (e.g., the magnitude or duration)as they propagate through the electrical/power system as this may beinferred from the energy-related signals and the location of each IED.In some embodiments, the energy-related signals captured or derived bythe plurality of IEDs may be stored on a memory device associated withthe plurality of IEDs, on a memory device associated with a diagnosticcomputing system, and/or on another memory device depending on theimplementation of the method 200 (e.g., on the at least one IED, on thediagnostic computing system, and/or on another device or system).

At block 215, it is determined if any alarms have been, or should be,triggered in response to the identified power events. In someembodiments, alarms may be triggered (e.g., automatically, orsemi-automatically) in response to the identified power events. Forexample, a load monitored by an IED in the electrical/power system mayhave an upper alarm threshold and/or a lower alarm threshold, and analarm (or alarms) may be triggered in response to voltage and/or currentsignals captured by the IED, e.g., at block 205, being above the upperalarm threshold and/or below the lower alarm threshold. An anomalousvoltage condition, for example, which is one example type of powerevent, corresponds to a measured IED voltage being above one or moreupper alarm thresholds or below one or more lower alarm thresholds. Insome embodiments, an alarm (or alarms) may be triggered in response tothe anomalous voltage condition. In some embodiments, the upper alarmthresholds and the lower alarm thresholds, e.g., associated withanomalous voltage condition and/or other power events, align with arecommended operational range of one or more loads, processes, and/orsystems monitored by the IEDs in the electrical/power system.

An alarm trigger may result in one or more portions (e.g., loads) of theelectrical/power system being controlled, e.g., automatically by theIED, diagnostic computing device, and/or other system(s) or device(s) onwhich the method 200 is implemented. For example, an alarm trigger mayresult in a load monitored by the IED being adjusted (e.g., turned off,or having one or more parameters adjusted).

Additionally, or alternatively, an alarm trigger may result in anotification or alert indicting the alarm being sent to one or moredevices or systems of the MCS, for example. In some embodiments, theMCS, or a user of the MCS, may take an action (or actions) in responseto the notification or alert. Example actions may include controllingthe above-mentioned one or more portions of the electrical/power system,or delaying, changing the sequence or even postponing a process inanother system (e.g., in a Power SCADA system, or in a buildingmanagement system, or in a manufacturing SCADA system, are but fewexamples). It is understood that other actions may, of course, beperformed.

At block 220, information relating to the identified power events and/oralarms is aggregated (e.g., the number of daily events or alarms, or thenumber of groups of time-wise-overlapping events, or the impact on thedownstream loads, are but few examples among many others). Theinformation relating to the power events and/or alarms may beaggregated, for example, for a particular time period or interval, e.g.,daily, as shown by plot 300 in FIG. 3, as will be described furtherbelow, or hourly, weekly, monthly, etc. Additionally, or alternatively,the information relating to the power events and/or alarms may beaggregated based on type of power event, type of alarm, meteringlocation(s), type of load, type of IED, criticality of an alarm to aparticular process or application, etc. The aggregated information mayindicate a number (or frequency) of occurrences of a power event and/oralarms for a particular time period. Additionally, the aggregatedinformation may indicate a number (or frequencies) of occurrences of apower event and/or alarms based on the type of power event, type ofalarm, metering location(s), type of load, type of IED, etc.

As one example, the aggregated information may include an aggregated sum(or count) of power events and/or alarms for a day, as shown in FIG. 3.The aggregated sum may, for example, indicate or be interpreted todetermine whether the number of power events and/or alarms is withinacceptable limits. Additionally, in some cases, the aggregated sum mayindicate whether an operator of a facility (e.g., 108, 110, 112, shownin FIG. 1) in which the at least one IED is capturing or derivingenergy-related signals, has lost or is at risk of losing control of thealarms of the facility (e.g., due to the aggregated sum being above anacceptable threshold). As one example, an aggregated sum of hundreds ofalarms over a one-day period may indicate the facility operator has lostcontrol of the facility's events/alarms. In some embodiments, theaggregated information (and the aggregated sum) may be stored on amemory device associated with the at least one IED, diagnostic computingsystem, and/or other system(s) or device(s) on which the method 200 isimplemented.

As apparent from discussions above, in some embodiments the aggregatedinformation may be plotted, as illustrated by plot 300 shown in FIG. 3.However, it is understood that in some embodiments the aggregatedinformation does not need to be plotted or otherwise visualized. Inembodiments in which the aggregated information is plotted, the plot maybe presented, for example, on a display device of a user device (e.g.,114, shown in FIG. 1) and/or a display device of another device of orassociated with the MCS and/or included in a report and/or anotification sent to a user.

At block 225, relevant event management groups and/or relevant eventand/or alarm periods are identified from the aggregated information. Inaccordance with various aspects of this disclosure, the event managementgroups include groups and/or sequences of the identified power events,and/or groups and/or sequences of alarm events triggered in response tothe identified alarms. Groups and/or sequences of the power events mayinclude, for example, groups and/or sequences of the power eventsoccurring over a particular time period, in a particular meteringlocation, etc. Additionally, groups and/or sequences of the alarm eventsmay include groups and/or sequences of alarm events occurring over aparticular time period, in a particular metering location, etc. Inaccordance with various aspects of this disclosure, the alarm eventscorrespond to events resulting from the triggering of multiple alarms,e.g., from the alarms identified at block 215.

The relevant event and/or alarm periods identified from the aggregatedinformation may be predetermined periods, e.g., daily periods, extractedfrom the aggregated information in some embodiments. Additionally, insome embodiments the relevant periods are identified or selected basedon detected changes in relevant data from the aggregated information. Asone example, the relevant periods may be identified based on breakpointsidentified in the aggregated information. Breakpoints (e.g., 320, 330,340, shown in FIG. 3) correspond to “significant” change points in theaggregated information, for example, separating one event and/or alarmperiod from a next event and/or alarm period. Additional aspects ofbreakpoints are described further below in connection with FIG. 4, forexample.

At block 225, outliers or extreme (or obvious) outliers may also beidentified in the aggregated information in some embodiments (e.g., forrefining the aggregated information prior to the event management groupsand/or event and/or alarm groups being identified from the aggregatedinformation, or to perform specific analysis on these groups ofoutliers, such as inferring patterns to provide the maintenance teamwith more actionable recommendations in a periodic report). As usedherein, “outliers” and “extreme outliers” refer to data in theaggregated information that does fit within normal boundaries oracceptable limits of the aggregated information (i.e., is not normaldata). To determine what is an “outlier” or an “extreme outlier”, thesystem may use simple statistical calculations and rules (e.g., thestandard approach of comparing each data points value with the medianvalue +/−1.5*IQR for an “outlier” and median+3*IQR for an “extremeoutlier” (of all the data points of the group/period). The IQR being theInter-Quantile Ratio, the value between the 75^(th) and the 25^(th)percentile) or some more advance techniques (e.g., DBSCAN or IsolationForest algorithms).

Referring again briefly to FIG. 3, the data at or around point 310 inthe plot 310 is an example of an extreme outlier. In some embodiments inwhich the aggregated information is analyzed to identify extremeoutliers in the information, data associated with the extreme outliersmay be removed from the aggregated information (e.g., prior to the eventmanagement groups and/or event and/or alarm groups being identified fromthe aggregated information). Removing such “outliers” or “extremeoutliers” is simple if in the previous step this distinction was madeand each of the outlier points/values was tagged as such (e.g.,“outlier” or “extreme outlier” tags). In some embodiments, the extremeoutliers are identified in each event and/or alarm period. For example,an extreme outlier in one period may not be an outlier in anotherperiod. As such, pattern analysis may show that some extreme outliersmay have been early warnings or the first “weak signals” of a new issue,generating many new events/alarms.

Returning to method 200, at block 230, which is optional in someembodiments, one or more actions may be triggered, avoided or postponedin response to or based on the event management groups and/or the eventand/or alarm periods identified at block 225. The actions, preventativeor otherwise, may affect at least one component of the electrical/powersystem, for example, a load (e.g., 120, shown in FIG. 1) in theelectrical/power system. As one example, the actions that are triggeredor postponed may include shutting down or turning on the at least onecomponent or adjusting one or more parameters associated with the atleast one component. For example, a threshold can be created so whenearth leakage current (i.e., current going to the ground) reaches thethreshold, at least one component responsible for the earth currentleakage may be shut down, and in some cases be disconnected (orotherwise decoupled) from the electrical/power system.

In some embodiments, the actions that are triggered or postponed mayalso include selectively interrupting power at one or more location inthe electrical/power system, generating an alarm or report, shutting offsomething in a manufacturing process or taking preventative action, as afew further examples. Additionally, the actions that are avoided mayinclude launching a specific process or step which would be normal inthe schedule, as an example. As one example of such “postponing a normalprocess steps” in a Power SCADA environment, not energizing a capacitorbank while voltage is already lower than normal due to some other load,process or action would be an illustration. As another example, delayingturning on the HVAC rooftop unit while some heavy motor is started inthe manufacturing plant may be an illustration in another domain.

In some embodiments, the actions that are triggered, avoided orpostponed are automatically performed by a control device of the MCS. Insome embodiments, the at least one IED responsible for capturing theenergy-related signals, e.g., at block 205, includes the control device.In other embodiments, one or more other portions of the MCS includes thecontrol device. For example, in some embodiments the diagnosticcomputing system of the MCS may include the control device.

Subsequent to block 230 (or 225), the method may end in someembodiments. In other embodiments, the method may return to block 205and repeat again. For example, in embodiments in which it is desirableto continuously (or semi-continuously) capture energy-related signalsand to dynamically analyze these captured energy-related signals forpower events and/or alarms, the method may return to block 205.Alternatively, in embodiments in which it is desirable to analyze asingle set of captured energy-related signals, for example, the methodmay end. In some embodiments in which the method ends after block 230(or 225), the method may be initiated again in response to user inputand/or a control signal, for example.

It is understood that method 200 may include one or more additionalblocks in some embodiments. For example, the method 200 may includeidentifying discriminant characteristics associated with the aggregatedinformation (i.e., the aggregated information from block 220), anddetermining a relative criticality score of each of the identifieddiscriminant characteristics to a process or an application associatedwith the electrical/power system, as described further below inconnection with FIG. 4, for example.

Referring to FIG. 4, a flowchart illustrates an example method 400 foridentifying discriminant characteristics in information related to powerevents and alarms. In accordance with some embodiments of thisdisclosure, method 400 is illustrative of example steps that may beperformed in connection with method 200 discussed above in connectionwith FIG. 2.

Similar to method 200, method 400 may be implemented on a processor ofat least one IED (e.g., 121, shown in FIG. 1A) in the electrical/powersystem, a processor of a diagnostic computing system (e.g., 125, shownin FIG. 1) in the electrical/power system, and/or remote from the atleast one IED and the diagnostic computing system.

As illustrated in FIG. 4, the method 400 begins at block 405, wherebreakpoints associated with the event and/or alarm periods discussedabove in connection with method 200, are identified in informationrelated to power events and alarms. In some embodiments, the informationrelated to power events and alarms corresponds to the aggregatedinformation from block 215 of method 200.

As briefly discussed above in connection with method 200, thebreakpoints (e.g., 510, 520, 530, shown in FIG. 5) correspond to“significant” change points in the aggregated information, e.g., for aparticular data set such as aggregated sum of power events and/oralarms, from one time period (N) to a next time period (N+1). Forexample, the significant change points may be rules based such ascorrespond to points in the aggregated data in which a significantincrease (e.g., a greater than fifty percent increase) in a number ofoccurrences of power events and/or alarms from one time period to a nexttime period is observed. Or it may be a statistical threshold (e.g.,using the “extreme outlier” threshold value of median+3*IQR).

In some embodiments, the breakpoints are identified or selected, forexample, using a machine learning algorithm. The machine learningalgorithm (self-learning, self-organizing, self-identification) mayoptimize the number of changepoints. It may be a combination of severalmachine learning algorithms and rules. One example would optimize thenumber and place of the change points by minimizing the RSS (residualsum of square) and using a penalty score for more change points (theseare machine learning typical tasks, so are considered as state of theart) as well as using a rule such as defining a minimal duration of anyperiod (e.g., not less than 14 days as one example). FIGS. 3 and 5 werederived from such machine learning algorithms. In each of these cases,“significant changes” are used in the data science common usage of theword (e.g., statistically different mean or median values or differentregression lines for each one of the periods).

As briefly discussed above in connection with method 200, in someembodiments the relevant periods (e.g., P1, P2, P3, shown in FIG. 5) areidentified or selected based on the breakpoints. In particular, thebreakpoints may separate one event and/or alarm period from a next eventand/or alarm period, thereby providing for identification of the eventand/or alarm periods. This is done in both FIGS. 3 & 5 by using thenumber of daily events/alarms as the input data. In this embodiment,this quantitative data is fed into the machine learning algorithmssequences. In another embodiment, the daily number of separate sequencesof overlapping events (groups of events defined based on the start andend time stamps of the events) could be used.

At block 410, each of the identified event and/or alarm periods ismodeled, for example, as illustrated by plot 500 shown in FIG. 5. Inaccordance with some embodiments of this disclosure, modeling theidentified event and/or alarm periods includes determining a bestpossible model for each of the identified event and/or alarm periods,and modeling each of the identified event and/or alarm periods (e.g.,using again RSS optimization techniques) based on the determined bestpossible model. The best possible model (e.g., median, increasing,decreasing, etc.) for each of the identified event and/or alarm periodsmay be determined, for example, by comparing each event and/or alarmperiod of the identified event and/or alarm periods with a previousevent and/or alarm period of the identified event and/or alarm periods.For example, an impact of each event and/or alarm period on theelectrical/power system may be compared with the impact of a previousevent and/or alarm period on the electrical/power system to determinethe best possible model. Referring again to FIG. 5, the identified eventand/or alarm periods shown in blue indicate a decreasing number ofevents and/or alarms in the illustrated example. Additionally, theidentified event and/or alarm periods shown in red indicate anincreasing number of events and/or alarms, and the identified eventand/or alarm periods shown in green indicate a stable number of eventsand/or alarms, i.e., the events and/or alarms are relatively stable. Thelines connecting the points shown in FIG. 5 are based on a median valuein the illustrated embodiment.

At block 415, each of the modeled event and/or alarm periods isclassified or categorized, for example, as stable, rising, dropping,etc. based on an analysis of the modeled event and/or alarm periods.Referring briefly to FIG. 5, the modeled event and/or alarm periods maybe analyzed to determine if the trend of the number of events and/oralarms is increasing, decreasing, or stable from the start of each eventand/or alarm period (e.g., from a first breakpoint of the period, e.g.,510 shown in FIG. 5) to the end of each event and/or alarm period (e.g.,from a second breakpoint of the period, e.g., 520 shown in FIG. 5). Ifit is determined that the trend of the number of events and/or alarms isincreasing from the start of an event and/or alarm period to an end ofan event and/or alarm period, the event and/or alarm period may beclassified as rising. Additionally, if it is determined that the trendof the number of events and/or alarms is decreasing from the start of anevent and/or alarm period to an end of an event and/or alarm period, theevent and/or alarm period may be classified as dropping. Further, if itis determined that the frequency or number of events and/or alarms isstable from the start of an event and/or alarm period to an end of anevent and/or alarm period, the event and/or alarm period may beclassified as stable. The fact of looking for a trend may be done byusing local regression over a moving period, or any other suitablealgorithm. This enables the algorithm to identify sub-periods which maybe better modeled as stable periods, even if the original algorithm hadnot identified any changepoint within a period. The modeling may thusidentify additional changepoints.

In some embodiments, each of the modeled event and/or alarm periods isclassified through curve fitting techniques, for example, using one ormore statistical learning algorithms where slope or slope variations ofthe event and/or alarm periods are modeled. In one such embodiment alist of possible curves is used and the best matching curve (using againthe RSS as key metrics) is then selected for each period. Forillustration purposes only, a very simple list of such curves may bemade of linear regression, exponential functions and polynomialregressions. In another embodiment, a machine learning “curve fitting inR” algorithm is applied.

At block 420, discriminant characteristics are identified in each of themodeled event and/or alarm periods. The aim of this step is to explainwhat Example discriminant characteristics that may be identified mayinclude, for example, particular devices (e.g., IEDs) or locations(e.g., metering locations) in the electrical/power system associatedwith a “high” (or greater than “normal”) amount of power events and/oralarms in the electrical/power system. Additionally, examplediscriminant characteristics that may identify patterns may include daysof the week and/or daily times and/or seasons on which a significantnumber of power events and/or alarms occur. For example, it may beobserved that a “higher” (or greater than “normal”) amount of powerevents and/or alarms occur on Sundays at 5 PM. From this observance,further analysis may occur to determine why a “higher” (or greater than“normal”) amount of power events and/or alarms occur on Sundays at 5 PM.For example, it may be determined that a “higher” (or greater than“normal”) amount of power events and/or alarms occur on Sundays at 5 PMsince this is when updates (e.g., time sync operations, and/or softwareand/or hardware updates) typically occur in the electrical/power system.Additional aspects of identification of discriminant characteristics aredescribed further below in connection with FIGS. 6-7E, for example.

After block 420, the method may end in some embodiments. In otherembodiments, the method may return to block 405 and repeat again (e.g.,in response to receiving additional information related to power eventsand/or alarms). Similar to method 200, it is understood that method 400may include one or more additional blocks in some embodiments. Forexample, in some embodiments the method 200 may include determining arelative criticality score of each of the identified discriminantcharacteristics to a process or an application associated with theelectrical/power system, e.g., over a predetermined time period. Therelative criticality score may be based, for example, on an impact ofthe identified discriminant characteristics to the process or theapplication, e.g., over the predetermined time period. In someembodiments, the impact of the identified discriminant characteristicsis related to tangible or intangible costs associated with theidentified discriminant characteristics to the process or theapplication. In some embodiments, the relative criticality score ispresented on at least one plot of the at least one plot visuallyrepresenting the identified discriminant characteristics. Additionally,in some embodiments the relative criticality score may be usedprioritize responding to the identified alarms (e.g., at block 230 ofmethod 200).

Referring to FIGS. 6-6C, a flowchart illustrates an example method 600for identifying discriminant characteristics/parameters/dimensions (the“characteristic” is an observation directly inferred from the data,“parameter” is the name used in the electrical/power system due to thefact that a controller may use a parameter as input to a measurementsetting or to control the electrical/power system, both of these words,when used in any data science field analysis, become a dimension of theanalysis). In accordance with some embodiments of this disclosure,method 600 is illustrative of example steps that may be performed inconnection with method 200 discussed above in connection with FIG. 2and/or method 400 discussed above in connection with FIG. 4. As oneexample, method 600 may be illustrative of example steps performed atblock 420 of method 400. Similar to methods 200 and 400, method 600 maybe implemented on a processor of at least one IED (e.g., 121, shown inFIG. 1A) in the electrical/power system, a processor of a diagnosticcomputing system (e.g., 125, shown in FIG. 1) in the electrical/powersystem, and/or remote from the at least one IED and the diagnosticcomputing system.

As illustrated in FIG. 6, the method 600 begins at block 605 wheredimensions available in, or derived from, electrical measurement dataare identified. As discussed in connection with figures above,electrical measurement data may be from, or derived from, energy-relatedsignals captured or derived by at least one IED in an electrical/powersystem. As also discussed in connection with figures above, power eventsand alarms triggered in response to the power events may be identifiedfrom electrical measurement data, and information related to theidentified power events and alarms may be aggregated. In accordance withsome embodiments of this disclosure, dimensions, such as timedimensions, priority and criticality dimensions, count and identified ofmeter dimensions, type of meter dimensions, etc., may be identifiedfrom, or derived from, the electrical measurement data directly, or fromthe above-discussed aggregated information. Additionally, in accordancewith some embodiments of this disclosure, the dimensions may beidentified from, or be derived from other information related to theelectrical/power system in which the electrical measurement data ismeasured. The other information may include, for example, known systemconfiguration data (e.g., using the type of meters defined in a PowerMonitoring software, as a dimension. This is useful to look for apossible cause of an abnormal increase of events identified, includingwaveform captures, which may be explained by a firmware updateimplemented on a specific type of meter. The user or the system may knowthe date of the firmware update push, and use this as an additionalconfirmation of this possible use case/cause/source, explaining whatcreated this increase. This dimension may be the statistically mostdiscriminant dimension identified by the analysis. A report may bepresented to the user showing this identified discriminant dimension, aswell as the confirmation of the timely co-occurrence of the firmwarepush. It may then be up to the user/expert to identify if this is aproblem, such as a too sensitive default setting threshold value on sagsfor example, or if this update fixed a problem, explaining why someevents may have been missed before.

The above-discussed time dimensions may indicate, for example, time ofday (hours or minutes) of discriminant events (e.g., as shown in FIG. 7,as will be discussed further below), days of week of discriminant events(e.g., as shown in FIG. 7A, as will be discussed further below), etc.Additionally, the priority dimensions may include priority scores, orvalues, associated with responding to particular power events and/oralarms in the electrical/power system (e.g., as shown in FIG. 7B, aswill be discussed further below). Criticality dimensions may indicate,for example, criticality of a power event and/or an alarm to aparticular process or application, etc. In accordance with someembodiments, the criticality dimensions may be determined based on, orin response to, the relative criticality score discussed above inconnection with method 200. The way the system identifies the mostdiscriminant dimension may be in one possible implementation, by usingthe “outlier” or “extreme outlier” rules or statistical or machinelearning algorithms (e.g., checking if a particular day of the weekexplains by itself more than 50% of all the events/alarms).

Count and identification (or spread) of meter dimensions may indicate,for example, how many meters (e.g., IEDs) are associated with orresponsible for detecting a particular percentage of events in theelectrical/power system, which meters are in the top ten for detectingevents, etc. (e.g., as shown in FIG. 7C, as will be discussed furtherbelow). Additionally, type(s) of meters dimensions may indicate types ofmeters associated with, or proximate to, detected events in theelectrical/power system. The types of meters may include, for example,PM8000, ION7650, CM4000t, and other metering devices provided bySchneider Electric. In some embodiments, the types of meters aredetermined by a source identification number of the meters (e.g., asshown in FIG. 7D, as will be discussed further below), and/or locationsof the meters in the electrical/power system It is understood that theabove example dimensions are but a few of many potential dimensionswhich may be identified at block 605.

At block 610, dimensions are extracted from events identified from theelectrical measurement data. In some embodiments, the events identifiedfrom the electrical measurement data may correspond to power eventsidentified from electrical measurement data at block 210 of method 200,for example.

Example dimensions that may be extracted may include, for example,pre-event and post-event information from energy-related signals (orwaveforms) associated with the electrical measurement data. Aspreviously discussed, electrical measurement data may be from or derivedfrom energy-related signals captured by at least one IED in anelectrical/power system. Dimensions may also be extracted from eventtext labels, for example, which are re-use in further analysis and/ordisplayed on a plot illustrating the electrical measurement data and/orassociated identified events (e.g., extracting the two dimensions ofpower quality issue=sag, and phase=A, from the various different labels“VA sag”, “voltage dip ph.A”, “Phase A voltage sag”, all three meaningthe same thing=“Voltage Sag on Phase A). The event text labels may beautomatically generated in some embodiments, and manually (orsemi-automatically) generated in other embodiments to tag a group ofevents, or to re-tag in a non-ambiguous way, the events (e.g., call allvoltage sags on phase A, “Voltage Sag Phase A”).

Further example dimensions that may be extracted may include powerquality type(s) further distinctions/groups of the events identifiedfrom the electrical measurement data, for example, non-steady statepower quality types such as sag, swell, transient, etc. as onedimension, distinguished from another dimension of steady state powerquality types such as power factor, harmonics, etc. Protection types,such as those that may be used in triggering, avoiding or postponingtriggering one of more actions at block 230 of method 200, for example,may also be extracted from the electrical measurement data in someembodiments. Example protection types may include thermal trip, shorttrip, earth leakage trip, etc., as a few examples. It is understood thatother types of dimensions may be extracted from events identified in theelectrical measurement data.

In one embodiment, the dimensions are extracted by observing differencesbetween one phase and another phase for measurements (e.g., voltage sagmagnitude measurements) obtained or derived from measurements by eachmeter or IED in the electrical/power system.

At block 615, other dimensions may be extracted from events identifiedfrom the electrical measurement data and/or other systems (e.g., sensorsor devices) in the electrical/power system, for example, to identify asource (or sources) of problems in the electrical/power system. Theseother dimensions may include, for example, operation type(s), such asstatus changed from “on” to “off”, “maintenance on operation—start”,etc. The other dimensions may also include internal system warnings,such as a warning “DB full alarm” generated in response to a database ofone or more systems in the electrical/power system having a fulldatabase. Additionally, the other example dimensions may include powermonitoring health status, for example, of the power monitoring andcontrol system responsible for monitoring and controlling theelectrical/power system. The power monitoring health status mayindicate, for example, time sync loss, loss of communications, etc. Itis understood that further dimensions may be extracted.

In some embodiments, the other dimensions may be extracted fromautomatically detected configurations of the electrical/power system(e.g., number and type of IEDs, number and type of loads, etc.), forexample, from sensor data received from one or more sensors in theelectrical/power system that may be helpful for identifying dimensionsin the electrical/power system. One example of sensor would betemperature or vibrations measured on the electrical equipment/load(e.g., on a motor or a transformer). In some embodiments, the otherdimensions may also be extracted from electrical/power systemconfiguration data manually input by a user, for example, as may bereceived from a user device (e.g., 114, shown in FIG. 1) (e.g., alarm(non-) acknowledgement).

At block 620, discriminant dimensions, for example, from the dimensionsidentified or extracted at blocks 605, 610, and 615, are selectivelycombined or aggregated. For example, discriminant dimensions (e.g.,frequency of events per day) associated with one meter in theelectrical/power system may be combined with like discriminantdimensions (e.g., frequency of events per day) associated with anothermeter in the electrical/power system, to determine a combined number ofdiscriminant dimensions, e.g., frequency of events in theelectrical/power system per day. This can be done systematically bypairwise aggregating the dimensions as illustrated in FIG. 7 (e.g., aday of week dimension on the X axis is combined with a 30-minuteintervals dimension on the Y axis, the result being the number of eventsper 30-minute interval, per day of the week).

At block 625, independent dimensions are identified, for example, fromthe dimensions identified or extracted at blocks 605, 610, and 615. Forexample, it may be determined that dimensions associated with priorityscore of an event, e.g., as shown in FIG. 7B, are independent fromdimensions associated with metering device type, e.g., as shown in FIG.7C. One possible implementation to identify the independence ofdimensions is to calculate if every days of week, at 21h30, there aresignificantly more events, than the rest of the daily hours. The reversemay than be applied to identify the linked dimensions, calculating thatonly on Mondays, at 21:30 (e.g., 720) as well as some other distinctpoints across day time and day of week (e.g., 710, 730, 740, 750, 760)have significantly more events. Further checks or operations (e.g.,removing “extreme outliers” prior to this analysis) need to be performedto validate that it is not just one single day explaining this “linkeddimension”.

At block 630, linked dimensions are identified, for example, from thedimensions identified or extracted at blocks 605, 610, and 615. Forexample, days of week dimensions may be linked to dimensions associatedwith particular times of day, e.g., as shown in FIG. 7, in someembodiments.

After block 630, the method may end in some embodiments. In otherembodiments, the method may return to block 605 and repeat again (e.g.,in response to receiving additional electrical measurement data).Similar to methods 200 and 400, it is understood that method 600 mayinclude one or more additional blocks in some embodiments.

Referring to FIG. 7, a plot 700 illustrates a frequency of eventsoccurring during each hour of each day over a one-week period of time(i.e., Monday thru Sunday). Discriminant points (i.e., points that standout with extreme high values) are indicated by reference designators710, 720, 730, 740, 750, 760. By analyzing the patterns of thesediscriminant points, the system automatically deduces which dimensionsare discriminant or not (e.g., time per day, day of the week, or thecombination of both time per day and day of week), and if they areindependent or if they are linked. Referring to FIG. 7A, a plot 1700illustrates a frequency of events occurring during each day of theone-week period where no day of the week is discriminant. In theillustrated embodiment, data is aggregated for multiple devices in anelectrical/power system. However, in some embodiments, the data may beaggregated for a single device (FIG. 7D).

Referring to FIG. 7B, a plot 2700 illustrates a frequency of events inan electrical/power system per priority score. In accordance with someembodiments, events may have a priority score, for example, that isdefined within the meter or historian system (e.g., power monitoringsystem). Additionally, in accordance with some embodiments, eventpriority may be based on a range of priority score values. For example,low priority events (not really an alarm) may have a range of priorityvalues from 0-63 (indicated as 63 in FIG. 7B), and low/mid priorityevents (still not considered an alarm in most cases) may have a range ofpriority values from 64-127 (indicated as 127 in FIG. 7B). Additionally,mid priority events (e.g., events that impact system and result in oneor more alarms being triggered) may have a range of priority values from128-195 (indicated as 195 in FIG. 7B), and high priority events (e.g.,events that significantly impact system and result in one or more alarmsbeing triggered) may have a range of priority values from 196-255(indicated as 255 in FIG. 7B). For example, time sync issues may not beconsidered for certain locations as critical, so related events may notraise alarms and thus be tagged as a score of 63. A voltage sag, on thecontrary may be considered a high impact issue, and receive a score of196, while an interruption may receive a score of 255. With thisinformation, it is possible to diagnose when an issue occurred, and totrace the potential source of the issue. For example, can see thatbefore a software (or firmware) upgrade was applied to a meteringdevice, X number of issues existed, while after the software upgrade wasapplied, Y number of issues exist. If Y is greater than X, can assumethat there is an issue with the software upgrade that needs to beaddressed.

The information shown in FIG. 7B, for example, may be used to direct asystem's resources to events that are a high priority, e.g., formitigating or eliminating effects of the high priority event(s).Additionally, the information shown in FIG. 7B may be used to observeincreases in alarm priority from one period to a next period, and totake an action (or actions) in response to this increase (e.g., at block230 of method 200). The type of actions may depend, for example, on theimportance of acting fast or not, and associated costs (monetary andotherwise, e.g., opportunity costs) with acting or not acting.

Referring to FIG. 7C, a plot 3700 illustrates frequency of events perdevice type (here, metering devices provided by Schneider Electric). Asillustrated in this example, there are very few events associated with(e.g., detected by) metering type CM4000. As also illustrated, there isa fairly significant number of events associated with (e.g., detectedby) metering type PM5560. In one example configuration, CM2450, CM4000,and CM4250 meters are located proximate to an input of theelectrical/power system, PM5560 is located closer to the loads in theelectrical/power system, and VIP is located between CM2450, CM4000,CM4250 and PM5560. In this example configuration (using the informationprovided in plot 3700), it can be determined that most of theevents/alarms are not occurring at the input of the electrical/powersystem, but rather further down (i.e., downstream) in theelectrical/power system. As illustrated, using plots similar to plot3700 and a hierarchical analysis of where a metering device is in theelectrical/power system (e.g., as shown in FIG. 1B), it can bedetermined where in the electrical/power system the events are occurring(e.g., proximate to the electrical/power system input, or furtherdownstream). With this information, it may be possible to diagnose whenan issue occurred, and to trace the potential source of the issue.

Referring to FIG. 7D, a plot 4700 illustrates a frequency of events perdevice type, similar to plot 3700 discussed above. Here, however, thedevice type is indicated by source identification number. Asillustrated, one of the devices is associated with a significant numberof events compared with the rest of the devices.

Referring to FIG. 7E, a plot 5700 illustrates example linked dimensions(here, time, event frequency and priority score dimensions). Asillustrated, several discriminant dimensions are associated with lowerpriority events (e.g., 5750, 5760), and several discriminant dimensionsare associated with mid- and high-priority events (e.g., 5710, 5720,5730, 5740).

Referring to FIGS. 7F and 7G, plots 6700, 7700 show another example wayof illustrating dimensions. This illustrates another way of identifyinglinked and independent discriminant dimensions. The system usesdimension reduction algorithms, such as principal components analysis,as one implementation. By calculations, or visually for a experts ordata scientists' eye, groupings may become obvious. A simplifiedexplanation is that “the closer together, the more linked”, and thereverse, “the more isolated, the more independent” (e.g., FIG. 7G, thedaily time of 12:30 arrow is separate from all the other discriminantpoints). In accordance with some embodiments, each technique requiresits appropriate checks to make sure the system provides reliableanalysis and actionable recommendations which will help solve the issue.Any change should be cross-checked for effectiveness in someembodiments, and maintenance operation may be an additional input intothe system to create a learning/feedback loop, validating the analysisand measuring the effectiveness of the operations. This in turn makesthe system grow in intelligence, providing even smarter recommendations(e.g., including the effectiveness of the user/maintenance teams'corrective actions into the system).

Referring to FIG. 8, a flowchart illustrates an example method 800 foridentifying and analyzing discriminantcharacteristics/parameters/dimensions in an electrical/power system. Inaccordance with some embodiments of this disclosure, method 800 isillustrative of example steps that may be performed in connection withmethod 200 discussed above in connection with FIG. 2 and/or method 400discussed above in connection with FIG. 4, for example. As one example,method 600 may be illustrative of example steps performed at block 420of method 400. Similar to methods 200 and 400, method 800 may beimplemented on a processor of at least one IED (e.g., 121, shown in FIG.1A) in the electrical/power system, a processor of a diagnosticcomputing system (e.g., 125, shown in FIG. 1) in the electrical/powersystem, and/or remote from the at least one IED and the diagnosticcomputing system.

As illustrated in FIG. 8, the method 800 begins at block 805 wherediscriminant analysis is conducted per dimensions, for example, fromdimensions identified from electrical measurement data (e.g., at block605 of method 600 shown in FIG. 6), dimensions associated with eventsidentified from the electrical measurement data (e.g., at block 610 ofmethod 600), and/or other dimensions associated with events and/orsystem in the electrical/power system (e.g., at block 615 of method600). As discussed above in connection with FIG. 6, for example,dimensions identified from electrical measurement data (e.g., electricalmeasurement data from, or derived from, energy-related signals capturedby at least one IED in the electrical/power system) may include timedimensions, priority and criticality dimensions, count andidentification of meter dimensions, type of meter dimensions, etc. Asalso discussed above in connection with FIG. 6, dimensions associatedwith (or extracted from) events identified from the electricalmeasurement data may include pre-event and post-event information fromenergy-related signals (or waveforms) associated with the electricalmeasurement data, dimensions extracted from event text labels, powerquality type(s) of the events identified from the electrical measurementdata, protection types, etc. As further discussed above in connectionwith FIG. 6, other dimensions associated with events and/or system inthe electrical/power system may include operation type(s), internalsystem warnings, monitoring health status, etc. The power monitoringhealth status may indicate, for example, time sync loss, loss ofcommunications, etc. It is understood that further dimensions may beextracted. It is understood that other dimensions may be considered inconducting the discriminant analysis at bock 805.

In accordance with some aspects of this disclosure, conductingdiscriminant analysis includes (among all the other previously describedprocesses, methods, calculations and results and examples, and notlimited to any or all of these) finding the linked dimensions of daytime (e.g., 30 minute intervals) and of day of week (e.g., on Mondays)described in the descriptions of and illustrated in FIG. 7, specificallyillustrate here by the reference 710. It is understood that otherexample processes for conducting discriminant analysis may occur.

In accordance with some aspects of this disclosure, conductingdiscriminant analysis per dimension, focusing on each single period (oron other sub-groups such as certain hours of each day e.g., morning,afternoon, night time periods), may provide for deeper relevant analysisof discriminant dimensions (e.g., than conducting discriminant analysisfor a full day). The analysis per period may be a preparation step offurther analysis steps, such as is the case in this exemplary flowdiagram where 805 is preparation for 810 and then for 815.

At block 810, the combination of all (or substantially all) thediscriminant dimensions are analyzed, for example, to enableidentification of what is discriminating for each period versus otherperiods at block 815. For example, returning briefly to FIG. 5, thecombination of discriminant dimensions associated with period 5 may becompared to the combination of discriminant dimensions associated withperiod 4, to identify what is discriminating in period 5 versus period4. In one embodiment, all periods data may be used as input to anoverall discriminant analysis (e.g., considering all data as oneperiod). In this case, only discriminant dimensions which arediscriminant across all periods will be identified (e.g., these may becalled “constant discriminant dimensions” list). In another embodiment,all the different discriminant dimensions identified in each of theperiods, may be combined into a list of discriminating dimensions (e.g.,“all active, at least in one period, or across several or all periods,discriminant dimensions” list).

At block 820, the differences between one period (e.g., period 5) and anext period (e.g., period 4), for each of the periods, may bequantified. For example, it may be determined that period 5 has three(or another number) more new discriminant dimensions than period 4. Thismay in one embodiment aim at disaggregating the “inherited” discriminantdimensions form each one of the previous periods. The goal in this casecould be to provide a “historical steps of unresolved issues” diagnosticreport to help a new user who arrived on the site after the previousexpert retired, to gain understanding of the number and importance ofthe ongoing events. This example may identify which previous periods'event types become the new normal, when not resolved. A typical graph ofsuch a case would be a “step by step” increasing number of events.Reference 820 would then show how many of the events would be added fromone period to the next. In such a case, again additional assumptionswould need to be confirmed such as subtracting the previous periodsnumbers of events and checking that the same discriminating dimensionscontinue on, even if more and more blurred by the addition of the nextunresolved issues. At block 825, discriminant characteristics, such asthose discussed in connection with figures above and described in theprevious example of step wise increments of quantities related tounresolved issues piling on each other, are identified based on thequantified differences.

In some embodiments, at block 830 (which is optional in someembodiments), the discriminant characteristics associated each periodand/or information associated with the discriminant characteristics(such as priority scores as shown in FIGS. 9 and 9A, as will bediscussed further below), may be presented, for example, on a plot. Theplot may display or indicate, for example, if the number of eventsincreased or decreased from one period to a next period, and by how muchthe number of events increased or decreased (in embodiments in which thenumber of events increased or decreased). The plot may be presented on adisplay device of a user device (e.g., 114, shown in FIG. 1, forexample), or another device of or associated with the monitoring andcontrol system closed herein, for example. A decrease may thus beanalyzed in the same way, for example: Was any of the previous existingissues solved? Was it the most recent previous period, or some otherolder previous period related issue? Or was it a constantly existingdiscriminant dimension (it's related real issue)?

After block 830, the method may end in some embodiments. In otherembodiments, the method may return to block 805 and repeat again (e.g.,in response to receiving additional electrical measurement data).Similar to methods 200, 400 and 600, it is understood that method 800may include one or more additional blocks in some embodiments.

Referring to FIGS. 9 and 9A, plots 900 and 1900 illustrate two exampleways in which information associated with discriminant characteristicsmay be plotted (e.g., at block 830 of method 800). As illustrated inplot 900 shown in FIG. 9, for example, information associated with thediscriminant characteristics (here, frequency of events and priorityscores) may be plotted for each period over a week timeframe. In theillustrated embodiment, there are four periods. As shown, during thefirst period, there are many low priority events (indicated by 63 in thefigure). As also shown, during the second period, there are many low/midpriority events (indicated by 127 in the figure).

Referring also to plot 1900 shown in FIG. 9A, this plot illustrates inanother similar example how the information shown in plot 900 may bepresented in a different manner (e.g., after removing the days of weekdimensions which was not discriminant in this new example). Moreparticularly, it can be seen that most of the low priority events(indicated by 63 in the figure) occur during the first period, and mostof the low/mid priority events (indicated by 127 in the figure) occurduring the second period. From this information, causes of the eventsmay be determined (or potential causes may at least be narrowed down).For example, it may be determined that the low priority events mostoften occur when employees are starting their work for the day. Usingthis information and information about habits or customs of employeeswhen they start their work for the day (e.g., they run certain processesto begin their day), the cause(s) of the low priority events may beisolated.

Referring to FIG. 10, a flowchart illustrates an example method 1000 foridentifying and analyzing sequences of events/groups in anelectrical/power system. Similar to method 800, in accordance with someembodiments of this disclosure, method 1000 is illustrative of examplesteps that may be performed in connection with method 200 discussedabove in connection with FIG. 2 and/or method 400 discussed above inconnection with FIG. 4, for example. This is illustrative of anothertype of aggregation and analysis. It may be conducted in parallel and inaddition to the previously used examples of aggregating the number ofevents/alarms per day. Or it may be conducted independently of thepreviously described aggregations and analysis. In this particularembodiment, the analysis focuses on the groups of overlapping events(aggregation of number of events/alarms is done per sequences ofoverlapping events (SooE), and not per day). The goal is to identifypatterns, trends, clusters of these sequences of overlapping events(groups of similar SooE, distinctively different from other groups ofSooE). If not done independently, but in conjunction with the previousanalysis, the system in one embodiment may consider the SooE analysis asa specific discriminant dimension of the previous daily aggregation(e.g., using the discriminant clusters as one-dimension analysis in thedaily analysis. Technically this may be done by using each cluster inthe same way as each one of the power quality types). Similar to methods200 and 400, method 1000 may be implemented on a processor of at leastone IED (e.g., 121, shown in FIG. 1A) in the electrical/power system, aprocessor of a diagnostic computing system (e.g., 125, shown in FIG. 1)in the electrical/power system, and/or remote from the at least one IEDand the diagnostic computing system.

As illustrated in FIG. 10, the method 1000 begins at block 1005 whereevents, such as the power events identified at block 210 of method 200,are grouped into overlapping sequences of groups/events. For example, itis possible for at least one of the events identified at block 210 tooverlap with, or occur in sequence with, at least one other eventidentified at block 210. In accordance with some embodiments, theoverlapping events or sequences of events may be grouped based onsimilarity (e.g., event type), proximity (e.g., location of event),etc., e.g., using a hierarchical approach of clustering. It isunderstood that these events may be grouped based on any number ofcharacteristics.

At block 1010, all (or substantially all) dimensions may be “pulled in”or otherwise extracted or identified from each of the groups formed atblock 1005. In some embodiments, the dimensions may include bothdiscriminant and non-discriminant dimensions.

At block 1015, power quality profiles may be identified for each of thegroups (and subparts of the groups in some instances), for example,using a power quality profile library. In accordance with someembodiments of this disclosure, the power quality profile library isstored on or accessed by the system(s) or device(s) on which method 1000is implemented. As one example, the power quality profile library may beaccessed from a storage device associated with a cloud-computing devicecontaining the latest power quality profiles.

At block 1020, pattern analysis techniques are used to identify patternsin the each of the groups, for example, based on the power qualityprofiled identified at block 1015. Some of the patterns were alreadyallude to previously: Daily hourly patterns (e.g., most SooE, or thelongest duration SooE, or the SooE with most alarms/events appear at17h30), days of week patterns (e.g., on Sundays). Other examples ofpatterns are seasonal patterns (e.g., temperature related, holidaysrelated), amongst many others. A simple calculation of which pattern hasthe most “explanatory capability” explains most cases, in the mostdistinctive way (from the other SooE clusters for example).

At block 1025, the groups (or subparts of the groups) as well as thediscriminant dimensions are analyzed for obvious or non-obvious links,possibly based on the identified patterns at block 1020. Examples oflinked dimensions are shown, for example, in FIGS. 11-11B, as will bedescribed further below. Link analysis techniques used in one embodimentis to apply a correlation analysis, and then apply a hierarchicalclustering to order the dimensions by proximity. This often visuallyhelps to show statistical proximity (groups=clusters co-occurring) aswell as statistical distance or “oppositions” one can visually observe(e.g., if this dimension is discriminant, then in “excludes” this otherdimension (e.g., calculated negative correlations, as in FIG. 1100 indark blue color).

At block 1030, sequence(s) analysis techniques are used to analyzeinternal sequences of the groups (e.g., the SooE cluster 1 may show apattern of sags, followed by interruptions), as well as between thedifferent SooE (e.g., a pattern of a cluster 1 SooE generally followedlater by a SooE of cluster 2), possibly the ones linked at block 1025.

After block 1030, the method may end in some embodiments. In otherembodiments, the method may return to block 1005 and repeat again (e.g.,in response to additionally power events being identified). Similar tomethods 200, 400, 600 and 800, it is understood that method 1000 mayinclude one or more additional blocks in some embodiments.

Referring to FIGS. 11-11B, plots 1100, 11100, and 21000 illustrate linkanalysis, for example, as may occur at block 1025 of method 1000discussed above. As illustrated in each of these figures, patterns ofgroups (and subgroups in some instances) stick out pretty clearly. Deepblue means you are above 0.8 in terms of correlations, which means thereis a very strong correlation between the groups and/or subgroups.Additionally, deep red means you are below −0.8 in terms of negativecorrelations, which means that it is very significant exclusive of theother dimension(s) or groups (e.g., a cluster). This type of analysiscan be conducted with increasing depth (e.g., 1100 shows less dimensionsthan 11100). Thus, it is important to distinguish high-level dimensions(e.g., 1100 only keeping few discriminant dimensions) from a morecomplete or more detailed analysis (e.g., 21100 where the system maykeep not only the discriminant dimensions, but even the values such asthe daily hourly of 21:30 previously mentioned in one of the examples).This enables to see non-obvious links (of links or of exclusions).Exclusions are sometimes the most useful to experts to identify theprobable source/cause (e.g., knowing a PQ event is not due to a faultmay guide him to search for correlations (co-occurrences) in thebuilding management system, which may not yet be linked to this presentsystem).

Referring to FIG. 12, plot 1200 illustrates the concept of daily/closeto real time alarms and diagnostics in accordance with embodiments ofthis disclosure. In some embodiments, an alarm may include a warning anda diagnostic. Example warnings may include, for example, a first warningindicating moved over threshold (is extreme day), and a second warning(which is optional in some embodiments) indicating moved above anyprevious existing day. Example warnings may also include a third warningindicating on demand current status during the day (after warning 1),and a fourth warning indicating daily sum of alarms. For each alarm,available and significant diagnostics may be provided, for example, toindicate new issues, few or spread meters, specific types of meteters,etc.

In accordance with some aspects of this disclosure, a “extreme outlierday” may be detected by the electrical/power system either at a specificlocation (e.g., at a specific IED), or at the aggregated level of allmonitored points (e.g., all the IEDs and all the electrical devicescapable of monitoring or creating events or alarms). The systemdynamically defines for each IED and for the global system (as well asfor other relevant locations in the electrical/power system hierarchy),a threshold of what is the value which defines an “extreme outlier day”as described previously (e.g., using the median value+3*IQR, of thecurrent/active period, so of the period for which no new change pointhas been determined active since the last identified change point). Ifduring any day, the number of events exceeds this threshold, the systemmay issue a warning in some embodiments. This warning may be an alarmvisualized in the mobile application on the user devices, or a messagesent per SMS or email.

In accordance with another implementation, the user may request a“current state diagnostic” after receiving a notification of an “extremeoutlier day”. The system will then run all the discriminating analysisas previously described. The main difference is that it will take thecurrent state of this day as if it was a new period following thecurrent one. In FIG. 12 there is only one period identified on the1^(st) day of extreme outlier. In some embodiments, the system will runall the analysis to compare the one period (as if it had stopped theprevious day=yesterday in a real-time system), and compare to it thecurrent day (e.g., identifying for this day the discriminant dimensions,then compare the differences with period 1 as if this day was in fact aperiod 2). This then may result in finding what major differences thereare, where the user may need to focus (e.g., a specific meter? Aspecific location ? A specific time? A specific type of meter? Etc.).This report may, in some embodiment, be directly generated and sent withthe first alert of “extreme outlier day”.

In some embodiment, a second alarm threshold may be defined in a system,“remembering” the worst day ever of the past. This may create anadditional alarm if during an “extreme outlier day”, this secondthreshold is also exceeded. This may in turn trigger additional actions,such as sending a notification to more users/managers as one exampleamong others.

In another embodiment, the system may send the next day a summary reportof such an “extreme outlier day” to a pre-defined list of users. Thismay include a trend evaluation (e.g., applying predictive algorithms andtrends analysis, especially if the issue was detected late the previousday, and is constantly accelerating).

As described above and as will be appreciated by those of ordinary skillin the art, embodiments of the disclosure herein may be configured as asystem, method, or combination thereof. Accordingly, embodiments of thepresent disclosure may be comprised of various means including hardware,software, firmware or any combination thereof.

It is to be appreciated that the concepts, systems, circuits andtechniques sought to be protected herein are not limited to use in theexample applications described herein (e.g., power monitoring systemapplications) but rather, may be useful in substantially any applicationwhere it is desired to manage smart alarms in an electrical system.While particular embodiments and applications of the present disclosurehave been illustrated and described, it is to be understood thatembodiments of the disclosure not limited to the precise constructionand compositions disclosed herein and that various modifications,changes, and variations can be apparent from the foregoing descriptionswithout departing from the spirit and scope of the disclosure as definedin the appended claims.

Having described preferred embodiments, which serve to illustratevarious concepts, structures and techniques that are the subject of thispatent, it will now become apparent to those of ordinary skill in theart that other embodiments incorporating these concepts, structures andtechniques may be used. Additionally, elements of different embodimentsdescribed herein may be combined to form other embodiments notspecifically set forth above.

What is claimed is:
 1. A method for managing smart alarms in anelectrical system, the method comprising: processing electricalmeasurement data from or derived from energy-related signals captured orderived by at least one intelligent electronic device (IED) of amonitoring and control system (MCS) to identify at least one of powerevents in the electrical system, or alarms triggered in response to anyidentified power events; aggregating information related to theidentified power events and the identified alarms; identifying relevantevent and/or alarm management groups and/or relevant event and/or alarmperiods from the aggregated information, the event management groupsincluding groups and/or sequences of the identified power events, and/orgroups and/or sequences of alarm events triggered in response to theidentified alarms; and triggering, avoiding or postponing triggering ofone or more actions in response to the identified event managementgroups and/or the identified event and/or alarm periods.
 2. The methodof claim 1, wherein the energy-related signals captured or derived bythe at least one IED include at least one of: voltage, current, energy,active power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, line voltages and power factor. 3.The method of claim 1, wherein identifying the power events includesidentifying power quality event types of the of the power events, thepower quality event types including at least one of: a voltage sag, dipor undervoltage, a voltage swell or overvoltage, a voltage or currenttransient, impulse or spike or notch, a temporary interruption oroutage, a voltage unbalance, a voltage flicker, a voltage or currentharmonic distortion, a phase shift, a frequency fluctuation, or othervoltage or current fluctuations or disturbance (including noise).
 4. Themethod of claim 1, wherein at least one of the alarms is triggered inresponse to the electrical measurement data being above one or moreupper alarm thresholds or below one or more lower alarm thresholds, orat least one of the alarms is triggered in response to multiple powerevents or event groups or event periods or event sequences of theidentified power events.
 5. The method of claim 1, wherein each IED ofthe at least one IED is installed or located at a respective meteringpoint of a plurality of metering points in the electrical system, andthe energy-related signals captured by each IED are associated with therespective metering point.
 6. The method of claim 5, wherein at leastone load is installed or located at each metering point of the pluralityof metering points, and each IED is configured to monitor the at leastone load installed or located at the respective metering point at whichthe IED is installed or located, wherein the energy-related signalscaptured by the IED are associated with the at least one load.
 7. Themethod of claim 1, wherein the information related to the identifiedpower events and the identified alarms is aggregated based on at leastone of: locations of the identified power events in the electricalsystem, geographic location, application and/or usage, time period(s) orinterval(s) or co-occurrences, criticality of the identified alarms to aparticular process or application, and device type(s) of the at leastone IED.
 8. The method of claim 1, further comprising; identifyingdiscriminant characteristics in the events and/or alarms and/or groupsand/or sequences and/or periods and/or aggregated information.
 9. Themethod of claim 8, wherein identifying discriminant characteristics inthe aggregated information, comprises: identifying breakpointsassociated with the event and/or alarm periods; modeling each of theevent and/or alarm periods; classifying each of the modeled event and/oralarm periods; and identifying discriminant characteristics in each ofthe modeled event and/or alarm periods.
 10. The method of claim 9,wherein the event and/or alarm periods are identified based on detectedchanges in relevant data from the aggregated information, and thebreakpoints associated with the event and/or alarm periods correspond tosignificant change points in the aggregated information separating oneevent and/or alarm period from a next event and/or alarm period of theevent and/or alarm periods.
 11. The method of claim 9, wherein modelingeach of the event and/or alarm periods, comprises: determining a bestpossible model for each of the event and/or alarm periods; and modelingeach of the event and/or alarm periods based on the determined bestpossible model.
 12. The method of claim 11, wherein the best possiblemodel is determined by comparing each event and/or alarm period of theevent and/or alarm periods with a previous event and/or alarm period ofthe event and/or alarm periods.
 13. The method of claim 12, wherein animpact of each event and/or alarm period on the electrical system iscompared with the impact of a previous event and/or alarm period on theelectrical system to determine the best possible model, or to use asdiscriminant characteristic of each event and/or alarm period in furtheranalysis, determined action or visualization.
 14. The method of claim 9,wherein each of the modeled event and/or alarm periods is classified asstable, rising or dropping based on an analysis of the modeled eventand/or alarm periods.
 15. The method of claim 9, wherein the each of theevent and/or alarm periods is classified through curve fittingtechniques using one or more statistical or machine learning algorithmswhere slope or slope variations of the event and/or alarm periods aremodeled.
 16. The method of claim 8, further comprising: determining arelative criticality score of each of the identified discriminantcharacteristics to a process or an application associated with theelectrical system over a particular time period.
 17. The method of claim16, wherein the relative criticality score is based on an impact of theidentified discriminant characteristics to the process or theapplication over the particular time period.
 18. The method of claim 17,wherein the impact of the identified discriminant characteristics isrelated to tangible or intangible costs associated with the identifieddiscriminant characteristics to the process or the application.
 19. Themethod of claim 16, further comprising: using the determined relativecriticality score to prioritize responding to the identified alarms. 20.The method of claim 8, further comprising: visually representing theaggregated events and/or alarms with their related information,groupings, periods, identified discriminant characteristics on at leastone plot, wherein the at least one plot is presented in a report, or ona display device of at least one of the at least one IED and otherdevices of the MCS.
 21. The method of claim 9, further comprising:triggering relevant alarms or actions by comparing a real time or timeaggregated status of number and/or type of events and/or alarms and/orgroups and/or sequences and/or periods and/or any aggregated informationor discriminant characteristic, to a derived threshold or type from themodel of any of the event and/or alarm periods.
 22. The method of claim9, further comprising: derive from the groupings and the discriminantcharacteristics the actionable information and recommendations for thesystem users to reduce the number of events/alarms in a report orthrough an IED or any of the components of the MCS or any other systemconnected to the MCS.
 23. The method of claim 1, wherein the identifiedevents and/or the identified alarms are enriched with the normalbehavior profiles derived from waveform captures associated with theenergy-related signals and then are used as comparison for thediscriminant dimensions identification and groupings.
 24. The method ofclaim 1, wherein the actions that are triggered or postponed include atleast one of: shutting down or turning on at least one component in theelectrical system, adjusting one or more parameters associated with theat least one component, selectively interrupting power at one or morelocations in the electrical system, shutting down or turning on aspecific related process in a building management system or in amanufacturing SCADA system, or generating an alarm or sending a statuschange re-usable by other control systems or generating a report. 25.The method of claim 24, wherein the at least one component includes atleast one load in the electrical system.
 26. The method of claim 1,wherein the actions that are avoided include launching a specificprocess step which would be normal in the schedule.
 27. The method ofclaim 1, wherein the actions are automatically performed by a controldevice of the MCS.
 28. The method of claim 27, wherein the at least oneIED includes the control device, or the at least one IED iscommunicatively coupled with the control device.
 29. A monitoring andcontrol system (MCS) for managing smart alarms in an electrical system,the MCS comprising: at least one intelligent electronic device (IED)including a processor and memory coupled to the processor, the processorand the memory configured to: process electrical measurement data fromor derived from energy-related signals captured or derived by the atleast one IED to identify power events in the electrical system, and toidentify alarms triggered in response to the identified power events;aggregate information related to the identified power events and theidentified alarms; identify relevant event management groups and/orrelevant event and/or alarm periods from the aggregated information, theevent management groups including groups and/or sequences of theidentified power events, and/or groups and/or sequences of alarm eventstriggered in response to the identified alarms; and trigger, avoid orpostpone triggering of one or more actions in response to the identifiedevent management groups and/or the identified event and/or alarmperiods.
 30. The system of claim 29, wherein the at least one IEDincludes a plurality of IEDs arrange in a hierarchical configuration inthe electrical system.
 31. The system of claim 30, wherein each IED ofthe plurality of IEDs is communicatively coupled to other IEDs of theplurality of IEDs, and each IED is configured to share electricalmeasurement data from or derived from energy-related signals derived orcaptured by the IED with the other IEDs.
 32. The system of claim 31,wherein the shared electrical measurement data is processed to identifythe power events in the electrical system, and to identify the alarmstriggered in response to the identified power events.
 33. The system ofclaim 29, wherein the MCS includes at least one user device incommunication with the at least one IED.
 34. The system of claim 33,wherein the at least one user device is capable of configuring the atleast one IED.
 35. The system of claim 29, wherein the processor and thememory of the at least one IED are further configured to: determine arelative criticality score of each of the identified discriminantcharacteristics to a process or an application associated with theelectrical system over a particular time period; and use the determinedrelative criticality score to prioritize responding to the identifiedalarms.
 36. A monitoring and control system (MCS) for managing smartalarms in an electrical system, the MCS comprising: at least oneintelligent electronic device (IED) configured to capture or deriveenergy-related signals in the electrical system; and a diagnosticcomputing system communicatively coupled to the at least one IED, thediagnostic computing system including a processor and memory coupled tothe processor, the processor and the memory configured to: processelectrical measurement data from or derived from the energy-relatedsignals captured or derived by the at least one IED to identify powerevents in the electrical system, and to identify alarms triggered inresponse to the identified power events; aggregate information relatedto the identified power events and the identified alarms; identifyrelevant event management groups and/or relevant event and/or alarmperiods from the aggregated information, the event management groupsincluding groups and/or sequences of the identified power events, and/orgroups and/or sequences of alarm events triggered in response to theidentified alarms; and trigger, avoid or postpone triggering of one ormore actions in response to the identified event management groupsand/or the identified event and/or alarm periods.
 37. The system ofclaim 36, wherein the at least one IED includes a plurality of IEDs, andaggregating information related to the identified power events and theidentified alarms includes: aggregating information related to theidentified power events and the identified alarms from the plurality ofIEDs.
 38. The system of claim 36, wherein the processor and the memoryof the diagnostic computing system are further configured to: determinea relative criticality score of each of the identified discriminantcharacteristics to a process or an application associated with theelectrical system over a particular time period; and use the determinedrelative criticality score to prioritize responding to the identifiedalarms.